Development of an outcome measure to assess performance of physiotherapy cardiorespiratory skills: A Delphi Study
Bibliographic record
Abstract
Current evidence regarding high-fidelity simulation (HFS) in physiotherapy education focuses on student perceptions producing very positive responses from students. HFS has the potential to improve skill performance and consequently better prepare students for gaining the best learning from clinical placement, but it is an expensive teaching method. Before the impact of HFS can be investigated, a valid and reliable tool to assess skill performance is required. The purpose of this study is to develop such a tool in order to assess student competency in core cardiorespiratory skills. The study implemented a Delphi method - a method of gaining convergence of opinion. Using pre-defined criteria, cardiorespiratory experts were identified and invited to participate. They were sent a questionnaire that used open questions in order to identify participants' expectations of students, in relation to core respiratory physiotherapy assessment and treatment techniques. Data from the first questionnaire ('round one') were analysed and used to generate the questionnaire for round two, in which participants were asked to indicate their level of agreement with various statements using Likert scales. The second round focused on: explanations about techniques to patients; instructions given prior to- and during techniques; hand positions used; positioning of patients, and; safety considerations. Round three will further clarify outstanding aspects, prior to the development of a draft outcome measure - this will then be circulated for comment in round four. The study will use the content validity index to calculate levels of agreement with statements in round four. In terms of results so far, there was a 31% (6/13) response rate for round one, representing Australia, Canada and the UK. Sixteen further panelists were identified for round two, following a search of staff lists and biographies from universities in all three countries. The response rate for round two was 51% (15/29), and round three is currently underway. The conference presentation will summarise conclusions from the study alongside presenting the outcome measure, ready for testing to establish its reliability and validity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".